CN115476881A

CN115476881A - Vehicle trajectory tracking control method, device, equipment and medium

Info

Publication number: CN115476881A
Application number: CN202211288639.5A
Authority: CN
Inventors: 雍文亮; 丁明慧; 胡旺; 王良; 周增碧
Original assignee: Chongqing Changan Automobile Co Ltd
Current assignee: Chongqing Changan Automobile Co Ltd
Priority date: 2022-10-20
Filing date: 2022-10-20
Publication date: 2022-12-16
Anticipated expiration: 2042-10-20
Also published as: CN115476881B

Abstract

The application provides a vehicle trajectory tracking control method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring target track parameters and state information of a vehicle; determining a preview distance according to the target track parameters and the vehicle speed information, and determining a preview deviation from a preview point to the target track according to the preview distance and the target track parameters; constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the preview distance and the preview deviation into the tracking control algorithm, and determining a first steering wheel turning angle value; an optimal control algorithm is constructed based on an adaptive weight control mode, the preview distance and the preview deviation are input into the optimal control algorithm, and a second steering wheel turning angle value is determined; the vehicle trajectory tracking method and the vehicle trajectory tracking device have the advantages that the first steering wheel angle value and the second steering wheel angle value are subjected to weighted fusion, the comprehensive steering wheel angle value is obtained, and the vehicle trajectory tracking is completed.

Description

Vehicle trajectory tracking control method, device, equipment and medium

Technical Field

The present application relates to the field of vehicle control or the field of automatic driving, and in particular, to a method, an apparatus, a device, and a medium for controlling vehicle trajectory tracking.

Background

With the high-speed development of automobile intellectualization, more and more automobiles are provided with automatic driving functions, people have higher and higher requirements on automatic driving control quality, and the automatic driving control quality of the automobiles is expected to be closer to real drivers so as to obtain more comfortable and credible driving experience. In autonomous driving, trajectory tracking control is one of the basic approaches to achieving lateral motion control of autonomous vehicles at level L3 and above.

However, the existing trajectory tracking control method adopts a single control algorithm, and the dynamic characteristics and the engineering application of the vehicle are not considered sufficiently, so that on one hand, the anthropomorphic degree is not enough, and the problem of being difficult to adapt to the scene of the whole working condition is solved; on the other hand, the automatic driving track tracking algorithm adopts a non-mechanism modeling mode and has strong dependence on a specific scene, so that the automatic driving track tracking algorithm cannot adapt to all working conditions to realize accurate tracking of the target track.

Content of application

In view of the above disadvantages of the prior art, the present application provides a method, an apparatus, a device, and a medium for controlling vehicle trajectory tracking, so as to solve the technical problem that accurate tracking of a target trajectory cannot be achieved under all operating conditions in vehicle trajectory tracking control.

In a first aspect, the present application provides a vehicle trajectory tracking control method, including:

acquiring target track parameters and state information of a vehicle, wherein the state information comprises current speed information of the vehicle;

determining a pre-aiming distance according to the target track parameter and the vehicle speed information, and determining a pre-aiming deviation from a pre-aiming point to a target track according to the pre-aiming distance and the target track parameter;

constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the preview distance and the preview deviation into the tracking control algorithm, and determining a first steering wheel angle value;

constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the pre-aiming distance and the pre-aiming deviation into the optimal control algorithm, and determining a second steering wheel turning angle value;

weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value;

and completing vehicle track tracking according to the control quantity corresponding to the comprehensive steering wheel turning angle value.

In an embodiment of the present application, the constructing a tracking control algorithm based on the understeer characteristics of the vehicle further includes:

determining an understeer characteristic coefficient of the vehicle correction track tracking according to a preset minimum vehicle speed threshold and current vehicle speed information;

and constructing a tracking control algorithm according to the understeer characteristic coefficient, the wheelbase of the vehicle, the steering angle transmission ratio, the pre-aiming distance and the association relationship of the pre-aiming deviation.

In an embodiment of the present application, the constructing an optimal control algorithm based on an adaptive weight control method, inputting the preview distance and the preview deviation into the optimal control algorithm, and determining a second steering wheel angle value further includes:

constructing a linear stationary inhomogeneous equation set expressed by a state space equation according to the lateral displacement, the lateral speed, the course angle and the yaw angular speed of the vehicle;

performing time domain conversion on the linear stationary inhomogeneous equation set to determine a measurement value of the equation set;

dividing the pre-aiming distance into a plurality of equivalent pre-aiming points at equal intervals based on the pre-aiming time corresponding to the pre-aiming distance, wherein the equivalent pre-aiming points correspond to different weight coefficients according to the distance of the pre-aiming distance;

constructing a performance index function of an optimal control algorithm according to the preview deviation of the equivalent preview point and the measured value;

performing derivation processing on the performance index function, inputting a weight coefficient corresponding to the equivalent preview point and the performance index function after derivation of the measured value, and determining an optimal control input quantity;

and determining the turning angle value of the second steering wheel according to the optimal control input quantity, the preview deviation and the steering angle transmission ratio.

In an embodiment of the present application, the expression of the first steering wheel angle value is determined as follows:

in the formula, K _v For an understeer coefficient of characteristics, δ _sw1 Is the first steering wheel angle value, L is the wheel base of the vehicle, i is the steering angle transmission ratio, v _xmin For a preset minimum threshold vehicle speed, pi is the circumferential ratio, d _prv For the pre-aiming distance, y _prv For preview deviation, v _x Is vehicle speed information.

In an embodiment of the application, the determining a preview distance according to the target track parameter and the vehicle speed information, and determining a preview deviation from a preview point to a target track according to the preview distance and the target track parameter further include:

determining the preview time of the vehicle based on the current vehicle speed information and the target track parameter of the vehicle;

calculating the current speed information and the preview time of the vehicle to determine a preview distance;

and calculating according to the pre-aiming distance and the target track parameters, and determining the pre-aiming deviation from a pre-aiming point to a target track.

In one embodiment of the present application, the expression of the performance indicator function is determined as,

in the formula, t _prv For preview time, y _prv For the preview deviation from the preview point to the target track, F (t), g (t) are intermediate variable functions, x ₀ The initial state at time t =0, u is the control input amount, and ω (t) is the angular frequency.

In an embodiment of the present application, the expression of the second steering wheel angle value is determined as follows:

in the formula, y _prvj For the preview deviation of the jth equivalent preview point to the target track, d _prv Is the pre-aiming distance, j is the jth equivalent pre-aiming point, n is the number of the equivalent pre-aiming points divided by the pre-aiming distance at equal intervals, F _j 、g _j As a function of an intermediate variable, t _prv For preview time, y _prv The preview deviation from the preview point to the target track, B and C are the input matrix and the output matrix of the state transition distance, x ₀ Initial state at time t =0, u _k For optimal control of input, omega _j For angular frequency, i is steering angle transmission ratio and pi is circumferential ratio.

In an embodiment of the present application, the weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value includes:

respectively determining a first weight coefficient corresponding to the tracking control algorithm and a second weight coefficient corresponding to the optimal control algorithm according to the current speed information of the vehicle and the curvature of a target track corresponding to the current position of the vehicle;

performing weighted calculation on the first steering wheel angle value and a first weight coefficient to determine a weighted first steering wheel angle value;

performing weighted calculation on the second steering wheel angle value and a second weight coefficient, and determining a weighted second steering wheel angle value;

and accumulating the weighted second steering wheel angle value and the weighted second steering wheel angle value to obtain a comprehensive steering wheel angle value.

In an embodiment of the application, the method further includes, according to the current vehicle speed information of the vehicle and a curvature on a target track corresponding to the current position of the vehicle, at least one of:

wherein v is _x As the current vehicle speed information of the vehicle,

p _ρ the weighting coefficients are respectively corresponding to the current speed and the current curvature of the vehicle, p is the curvature of the target track corresponding to the current position of the vehicle,

p _ρ determines the magnitude of the second weight coefficient.

In an embodiment of the present application, an expression corresponding to a target trajectory in the target trajectory parameters is determined as:

y＝A ₀ +A ₁ x+A ₂ x ² +A ₃ x ³

wherein y is the abscissa of the target trajectory, x is the ordinate of the target trajectory, A ₀ 、A ₁ 、A ₂ 、A ₃ The lateral deviation, the course angle, the road curvature and the road curvature change rate are sequentially included.

In an embodiment of the present application, the acquiring target trajectory parameters and state information of the vehicle further includes:

the state information comprises yaw angular speed information, vehicle speed information, steering wheel corner position and steering machine handshake state;

respectively preprocessing the target track parameters and the state information of the vehicle to obtain the preprocessed target track parameters and the preprocessed state information;

converting the target track parameters and state information of the vehicle; and/or performing outlier removing processing on the target track parameters and the state information of the vehicle; and/or filtering the target track parameters and the state information of the vehicle.

In an embodiment of the present application, before the obtaining the target trajectory parameter and the state information of the vehicle, the method further includes:

acquiring automatic driving activation information, planning state information and steering machine state information of the vehicle;

respectively determining states of automatic driving activation information, planning state information and steering machine state information of the vehicle;

and if the states of the automatic driving activation information, the planning state information and the steering engine state information are normal, the state verification is passed, and an enabling signal of trajectory tracking control is output.

In an embodiment of the present application, after completing the tracking of the vehicle trajectory according to the control quantity corresponding to the integrated steering wheel rotation angle value, the method further includes:

determining a steering wheel corner request value corresponding to the switched comprehensive steering wheel corner value based on the steering engine handshake state;

according to the vehicle speed information and the steering wheel corner position, safety judgment is carried out on the steering wheel corner request value;

if the requested value of the steering wheel corner is unsafe, carrying out assignment and rate limitation on the requested value of the steering wheel corner according to preset functional safety limitation until the requested value of the steering wheel corner is safe, and obtaining the final requested value of the steering wheel corner;

and filtering the final steering wheel angle request value and the track tracking control state, and outputting the filtered values.

In a second aspect, the present application provides a vehicle trajectory tracking control device, including:

the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring target track parameters and state information of a vehicle, and the state information comprises current speed information of the vehicle;

the preview module is used for determining a preview distance according to the target track parameter and the vehicle speed information and determining a preview deviation from a preview point to a target track according to the preview distance and the target track parameter;

the first control module is used for constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the preview distance and the preview deviation into the tracking control algorithm and determining a first steering wheel angle value;

the second control module is used for constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the preview distance and the preview deviation into the optimal control algorithm and determining a second steering wheel turning angle value;

the comprehensive control module is used for weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value;

and the track tracking module is used for finishing vehicle track tracking according to the control quantity corresponding to the comprehensive steering wheel turning angle value.

In a third aspect, the present application provides an electronic device comprising:

one or more processors;

a storage device for storing one or more programs that, when executed by the one or more processors, cause the electronic equipment to implement the vehicle trajectory tracking control method described above.

In a fourth aspect, the present application provides a vehicle device including the electronic device described above.

In a fifth aspect, the present application provides a computer readable storage medium having stored thereon computer readable instructions, which, when executed by a processor of a computer, cause the computer to execute the above-mentioned vehicle trajectory tracking control method.

The beneficial effect of this application: the method comprises the steps of constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the preview distance and the preview deviation into the tracking control algorithm, and determining a first steering wheel turning angle value; constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the pre-aiming distance and the pre-aiming deviation into the optimal control algorithm, and determining a second steering wheel turning angle value; weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value; on one hand, the trajectory tracking control algorithm based on the kinematics mechanism modeling is suitable for characteristic intervals with unobvious vehicle dynamics response, such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on the dynamics mechanism modeling is suitable for characteristic intervals with obvious vehicle dynamics response, such as high speed, large curvature and the like, fully considers the vehicle dynamics characteristics, and can adapt to accurate target track tracking under all working conditions through dual-mode control comprehensive weighting processing.

In addition, the understeer characteristic of the vehicle is controlled by a trajectory tracking control algorithm based on kinematics mechanism modeling, so that the trajectory tracking precision is improved; the optimal control algorithm based on the dynamics mechanism modeling adopts a self-adaptive weight control mode, and reduces the weight of a near-end preview point and improves the weight of a far-end preview point, so that the control frequency is reduced under the condition of ensuring the control precision as much as possible, the high-frequency jitter of control output is avoided, and the psychological trust of drivers and passengers is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:

FIG. 1 is a schematic diagram of an implementation environment of a vehicle trajectory tracking control method according to an exemplary embodiment of the present application;

FIG. 2 is a flow chart illustrating a vehicle trajectory tracking control method according to an exemplary embodiment of the present application;

FIG. 3 is a flow chart of step S230 in the embodiment shown in FIG. 2 in an exemplary embodiment;

FIG. 4 is a flow chart of step S240 in the embodiment shown in FIG. 2 in an exemplary embodiment;

fig. 5 is a block diagram showing the structure of a vehicle trajectory tracking control device according to an exemplary embodiment of the present application;

FIG. 6 is a schematic diagram of the construction of the vehicle trajectory tracking control apparatus shown in the embodiment shown in FIG. 5;

FIG. 7 illustrates a schematic structural diagram of a computer system suitable for use to implement the electronic device of the embodiments of the subject application.

Detailed Description

Other advantages and effects of the present application will become apparent to those skilled in the art from the disclosure herein, wherein the embodiments of the present application will be described in detail with reference to the accompanying drawings and preferred embodiments. The application is capable of other and different embodiments and its several details are capable of modifications and various changes in detail without departing from the spirit of the application. It should be understood that the preferred embodiments are for purposes of illustration only and are not intended to limit the scope of the present disclosure.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present application, and the drawings only show the components related to the present application and are not drawn according to the number, shape and size of the components in actual implementation, the type, quantity and proportion of each component in actual implementation may be changed freely, and the layout of the components may be more complicated.

In the following description, numerous details are set forth to provide a more thorough explanation of the embodiments of the present application, however, it will be apparent to one skilled in the art that the embodiments of the present application may be practiced without these specific details, and in other embodiments, well-known structures and devices are shown in block diagram form rather than in detail in order to avoid obscuring the embodiments of the present application.

Fig. 1 is a schematic diagram of an implementation environment of a vehicle trajectory tracking control method according to an exemplary embodiment of the present application. The vehicle includes one or more data collectors 11, trajectory planning information 12 (i.e., road network definition files), a computer 13, and one or more controllers 14. The vehicle is typically a land-based vehicle having three or more wheels, e.g., a passenger car, a light truck, or the like. The vehicle has a front, a rear, a left side and a right side, wherein the terms front, rear, left and right are understood from the perspective of an operator of the vehicle seated in the driver's seat in a standard operating position, i.e. facing the steering wheel.

The computer 13 generally includes a processor and memory, including one or more forms of computer-readable media, that store instructions executable by the processor for performing various operations. Further, the computer 13 may include and/or be communicatively connected to one or more other computing devices included in the vehicle for monitoring and/or controlling various vehicle components. The computer 13 is typically programmed and arranged for communication over a controller area network bus or the like.

The computer 13 may also have a connection to an on-board diagnostic connector (OBD-II), a CAN (controller area network) bus, and/or other wired or wireless mechanisms. Through one or more of such communication mechanisms, the computer 13 may transmit messages to and/or receive messages from various devices in the vehicle, such as controllers, actuators, sensors, etc., including the data collector 11 and the controller 14. Alternatively or additionally, in the case where the computer 13 actually includes a plurality of devices, a CAN bus or the like may be used for communication between the devices represented as the computer 13 in the present invention. Further, the computer 13 may be configured to communicate with other devices via various wired and/or wireless network technologies, such as cellular, bluetooth, universal Serial Bus (USB), wired and/or wireless packet-switched networks, and so forth.

The memory of the computer 13 typically stores the collected data. The collected data may include various data collected in and/or derived from the vehicle by the data collector 11. Examples of data collectors 11 may include, for example, data regarding the driving behavior of one or more vehicles, such as the location of the vehicle over time (e.g., geographic coordinates, distance to the vehicle, etc.), the speed of the vehicle over time, the direction of travel, the number and magnitude of changes in direction and speed at different points in time, and so forth. The collected data may further include, for example, information such as the type of vehicle or vehicles (e.g., pickup truck, passenger car, mini-van, etc.), size, make, model, etc. The collected data may additionally include data calculated from data received from data collector 11 in computer 13. In general, the collected data may include any data collected by the data collector 11, received through vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications, collected or received from other sources, and/or calculated from such data.

The computer 13 may be programmed to receive data from the data collector 11 and data regarding objects, such as the destination, route, arrival time, etc. of the vehicle. The computer 13 may further be programmed to collect data about the targets of the vehicle and other data related to the vehicle, such as a map of the area in which the vehicle is operating. For example, the computer 13 may receive input from the user via the user interface indicating the user's destination, the route the user wants to take, the driving style (conservative, sporty), and the like. The computer 13 may further comprise or receive a map of, for example, an area, for example from a global positioning system (GPS system) or from a memory. Based on the received data, the computer 13 may execute a so-called "mission plan", i.e. plan a path to a destination according to driving directions on a road network map. The computer 13 may further be programmed to store the data in a memory for further use, for example, in determining a driving strategy and/or driving the vehicle.

In general, each controller 14 may include a processor programmed to receive instructions from computer 13, execute the instructions, and send messages to computer 13. Further, the controllers 14 may each include sensors or otherwise operate as data collectors 11 to provide data to the computer 13 regarding vehicle speed, vehicle steering angle, height of suspension, and the like. For example, data corresponding to the brake pressure applied by the brake controller 14 may be sent to the computer 13.

The data collector 11 may comprise various means, for example, the data collector 11 may comprise components for sensing the environment, for example, a lidar, a radar, a video camera, an ultrasonic sensor, an infrared sensor for tracking the vehicle. The data collector 11 may further include components that collect data of dynamic vehicles, such as speed, yaw rate, steering angle, and the like. Furthermore, the above examples are not intended to be limiting. Other types of data collectors 11, such as accelerometers, gyroscopes, pressure sensors, thermometers, barometers, altimeters, etc. may be used to provide data to the computer 13.

The road network definition file may include a coded topological metric map of the road network in which the vehicle may operate. The topological metrology map includes latitude and longitude coordinates for road features and other objects in the environment and is encoded based on derivatives of the RNFD file format. The road network definition file may supply map data, for example to implement trajectory planning information to the computer 13.

The computer 13 may be programmed to store data relating to the vehicle. As described above, the data may include data representing a history of data points, such as the pose of the vehicle over time, the speed of the vehicle over time, the direction of travel, the number and magnitude of changes in direction and speed at different points in time, and the like.

The problems noted above have general applicability in general travel scenarios. It can be seen that the existing trajectory tracking control method adopts a single control algorithm, and the dynamic characteristics and the engineering application of the vehicle are not considered sufficiently, on one hand, the anthropomorphic degree is not enough, and the problem of being difficult to adapt to the scene of the whole working condition is solved; on the other hand, the automatic driving track tracking algorithm adopts a non-mechanism modeling mode and has strong dependence on a specific scene, so that the automatic driving track tracking algorithm cannot adapt to all working conditions to realize accurate tracking of the target track. To solve these problems, embodiments of the present application respectively propose a vehicle trajectory tracking control method, a vehicle trajectory tracking control apparatus, an electronic device, and a computer-readable storage medium, which will be described in detail below.

Referring to fig. 2, a flowchart of a vehicle trajectory tracking control method according to an exemplary embodiment of the present application is shown. The method can be applied to the implementation environment shown in fig. 1 and is specifically executed by the intelligent terminal in the implementation environment. It should be understood that the method may be applied to other exemplary implementation environments and is specifically executed by devices in other implementation environments, and the embodiment does not limit the implementation environment to which the method is applied.

In an exemplary embodiment, fig. 2 is a flowchart of a vehicle trajectory tracking control method shown in an exemplary embodiment of the present application, which is detailed as follows:

step S210, acquiring target track parameters and state information of the vehicle, wherein the state information comprises current speed information of the vehicle;

specifically, the state information includes yaw rate information, vehicle speed information, steering wheel angle position, and steering gear handshake state. The target track comprises a polynomial expression consisting of a lateral deviation, a heading angle, a road curvature and a road curvature change rate, the vehicle speed information is acquired by using a vehicle chassis sensor, for example, sensing data is acquired by an on-board sensor on the vehicle so as to acquire current vehicle speed information, for example, the current position of the vehicle is determined by the target track and the relative position of a coordinate system of the current position of the vehicle, for example, the electronic stability program system acquires a yaw rate error through an angle signal from a steering wheel angle sensor.

And determining an expression corresponding to the target track in the target track parameters as follows:

y＝A ₀ +A ₁ x+A ₂ x ² +A ₃ x ³

wherein y isThe abscissa of the target trajectory, x being the ordinate of the target trajectory, A ₀ 、A ₁ 、A ₂ 、A ₃ The lateral deviation, the course angle, the road curvature and the road curvature change rate are sequentially included.

Here, the vehicle includes, but is not limited to, a fuel automobile, an extended range electric vehicle, a pure electric vehicle, a hybrid vehicle, a hydrogen energy vehicle, and the like.

Step S220, determining a pre-aiming distance according to the target track parameter and the vehicle speed information, and determining a pre-aiming deviation from a pre-aiming point to a target track according to the pre-aiming distance and the target track parameter;

specifically, the preview time of the vehicle is determined based on the current vehicle speed information and target track parameters of the vehicle; calculating the current speed information and the preview time of the vehicle to determine a preview distance; and calculating according to the preview distance and the target track parameters, and determining the preview deviation from a preview point to a target track.

For example, the current preview point can be determined by using the current vehicle speed information and preview time of the vehicle. The preview theory can accurately reflect the control behavior of the driver, has simple structure and strong adaptability, is widely applied in the field of trajectory tracking, and adopts a method of fixing preview time to calculate the preview distance through the preview theory.

Step S230, constructing a tracking control algorithm based on the understeer characteristic of the vehicle, inputting the preview distance and the preview deviation into the tracking control algorithm, and determining a first steering wheel angle value;

specifically, the understeer characteristic of the vehicle is considered by using the improved pure tracking algorithm, and the track tracking precision is improved.

Step S240, an optimal control algorithm is established based on a self-adaptive weight control mode, the preview distance and the preview deviation are input into the optimal control algorithm, and a second steering wheel turning angle value is determined;

specifically, an improved optimal control algorithm is utilized, a self-adaptive weight control mode is adopted, and the weight of a near-end preview point is reduced and the weight of a far-end preview point is improved, so that the control frequency is reduced under the condition that the control precision is ensured as much as possible, the high-frequency jitter of control output is avoided, and the psychological trust of drivers and passengers is improved.

Step S250, weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value;

specifically, weighting and summing are carried out through the weight coefficients, weighting fusion can be achieved, a comprehensive steering wheel turning angle value is obtained, the comprehensive steering wheel turning angle value is used, a proper pre-aiming point is selected reasonably through the weight coefficients in the control process, and the fact that the vehicle can stably and accurately follow the track is guaranteed.

Step S260, completing vehicle track tracking according to the control quantity corresponding to the comprehensive steering wheel turning angle value;

in the embodiment, two control algorithms are established based on kinematics and a dynamics mechanism, on one hand, the trajectory tracking control algorithm based on kinematics mechanism modeling is suitable for characteristic intervals with unobvious vehicle dynamics response, such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on the dynamics mechanism modeling is suitable for characteristic intervals with obvious vehicle dynamics response, such as high speed, large curvature and the like, fully considers the vehicle dynamics characteristics, and can adapt to accurate target track tracking under all working conditions through dual-mode control comprehensive weighting processing.

In other embodiments, obtaining target trajectory parameters and state information of a vehicle further comprises:

converting the target track parameters and the state information of the vehicle; and/or performing outlier removing processing on the target track parameters and the state information of the vehicle; and/or, filtering the target track parameter and the state information of the vehicle, which is not described herein again.

Here, the pretreatment may be any one of the above-described treatment methods, or at least one of the treatment methods may be combined.

Specifically, the conversion processing, that is, converting the target trajectory parameter and the unit of the state information, corresponds to unit conversion.

Specifically, the outlier removing processing is to filter the target track parameters and the state information, determine outliers, and complete the outlier removing processing by eliminating the outliers.

Specifically, in a specific application scenario, a corresponding filtering rule may be configured for the filtering process, for example, the configured filtering rule may be: the total kilometer number is greater than N kilometer numbers, wherein N is a natural number greater than or equal to 1, and specific numerical values of N can be limited according to different application scenes, so that the vehicle speed information is limited.

Through the preprocessing, the accuracy of the target track is improved, and meanwhile, the accuracy of the state information is also improved.

In other embodiments, before obtaining the target trajectory parameter and the state information of the vehicle, the method further includes:

Here, it should be noted that the state of the planned state information is determined by the state information in the planned state information, the state information of the steering gear state information determines the state of the steering gear state information, and similarly, the state of the automatic driving activation information is determined by the state information included in the automatic driving activation information.

And when the state verification is passed, outputting an enabling signal of the track tracking control, for example, the enabling signal is an on signal, for controlling the track tracking controller to be in the working state, and conversely, when the state verification is failed, outputting an enabling signal of the track tracking control, for example, the enabling signal is an off signal, for controlling the track tracking controller not to be in the working state.

Through the mode, the activation judgment is carried out on the control track tracking controller according to the current state of the vehicle, the optimal control mode of the current vehicle can be determined according to local conditions, the robustness of vehicle track tracking control is improved, and the stability of the vehicle track tracking control is enhanced.

In other embodiments, please refer to fig. 3, which is a flowchart of step S230 in the embodiment shown in fig. 2 in an exemplary embodiment; wherein the constructing a tracking control algorithm based on the understeer characteristics of the vehicle further comprises:

step S310, determining an understeer characteristic coefficient of the vehicle correction track tracking according to a preset minimum vehicle speed threshold and current vehicle speed information;

and S320, constructing a tracking control algorithm according to the incidence relation among the understeer characteristic coefficient, the wheelbase of the vehicle, the steering angle transmission ratio, the pre-aiming distance and the pre-aiming deviation.

It should be noted that, the expression of the first steering wheel angle value is determined as follows:

in the formula, K _v For understeer coefficient of characteristics, δ _sw1 Is the first steering wheel angle value, L is the wheel base of the vehicle, i is the steering angle transmission ratio, v _xmin For a preset minimum threshold vehicle speed, pi is the circumferential ratio, d _prv For the pre-aiming distance, y _prv For preview deviation, v _x Is vehicle speed information.

By the method, the understeer characteristic of the vehicle is controlled by the trajectory tracking control algorithm based on the kinematics mechanism modeling, and the trajectory tracking precision is improved.

Referring to FIG. 4, it is a flowchart of step S240 in the embodiment shown in FIG. 2 in an exemplary embodiment; further comprising:

step S410, constructing a linear stationary inhomogeneous equation set expressed by a state space equation according to the lateral displacement, the lateral speed, the course angle and the yaw angular velocity of the vehicle;

step S420, performing time domain conversion on the linear stationary inhomogeneous equation set to determine a measurement value of the equation set;

step S430, based on the pre-aiming time corresponding to the pre-aiming distance, equally dividing the pre-aiming distance into a plurality of equivalent pre-aiming points, wherein the equivalent pre-aiming points correspond to different weight coefficients according to the distance of the pre-aiming distance;

step S440, constructing a performance index function of an optimal control algorithm according to the preview deviation of the equivalent preview point and the measured value;

specifically, the expression of the performance indicator function is determined as,

in the formula, t _prv For preview time, y _prv The preview deviation from the preview point to the target track is obtained, F (t) and g (t) are intermediate variable functions, and x ₀ The initial state at time t =0, u is the control input amount, and ω (t) is the angular frequency.

Step S450, performing derivation processing on the performance index function, inputting the weight coefficient corresponding to the equivalent preview point and the performance index function after derivation with the measured value, and determining the optimal control input quantity;

and step S460, determining the turning angle value of the second steering wheel according to the optimal control input quantity, the preview deviation and the steering angle transmission ratio.

Specifically, the expression of the second steering wheel angle value is determined such that,

in the formula, y _prvj For the preview deviation of the jth equivalent preview point to the target track, d _prv Is the pre-aiming distance, j is the jth equivalent pre-aiming point, n is the number of the equivalent pre-aiming points divided by the pre-aiming distance at equal intervals, F _j 、g _j As a function of an intermediate variable, t _prv For preview time, y _prv B and C are input matrix and output matrix of state transition distance, x, respectively ₀ Initial state at time t =0, u _k For optimal control of input, omega _j Is the angular frequency, i is the steering angle transmission ratio, and pi is the circumferential ratio.

In other embodiments, the weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a composite steering wheel angle value includes:

It should be noted that, the method further includes at least one of the following steps according to the current vehicle speed information of the vehicle and the curvature of the target track corresponding to the current position of the vehicle:

wherein v is _x As the current vehicle speed information of the vehicle,

p _ρ determines the magnitude of the second weight coefficient.

Through the mode, two control algorithms are established based on kinematics and a dynamics mechanism, on one hand, the trajectory tracking control algorithm based on kinematics mechanism modeling is suitable for characteristic intervals with unobvious vehicle dynamics response, such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on the dynamics mechanism modeling is suitable for characteristic intervals with remarkable vehicle dynamics response, such as high speed, large curvature and the like, the vehicle dynamics characteristics are fully considered, the vehicle can stably and accurately follow the expected track under various complex working conditions through the dual-mode control comprehensive weighting processing, and the control precision of the vehicle is improved.

In other embodiments, after completing the tracking of the vehicle trajectory according to the control quantity corresponding to the integrated steering wheel angle value, the method further includes:

determining a steering wheel corner request value corresponding to the switched comprehensive steering wheel corner value based on the handshake state of the steering engine;

if the requested value of the steering wheel corner is unsafe, the requested value of the steering wheel corner is subjected to assignment and rate limitation according to preset functional safety limitation until the requested value of the steering wheel corner is safe, and the final requested value of the steering wheel corner is obtained;

and filtering the final steering wheel corner request value and the track tracking control state, and outputting the final steering wheel corner request value and the track tracking control state.

For example, the input signal of the safety limiting module is a steering wheel angle, a lateral acceleration, a vehicle speed and a steering wheel angle, the output signal of the safety limiting module is a state signal, the state signal is used for feeding back the safety state of the trajectory tracking controller to the state machine information module, and the safety limiting module is used for performing assignment and rate limitation on the steering wheel angle according to the lateral acceleration, the vehicle speed and the steering wheel angle to obtain a steering wheel angle request value meeting the functional safety and transmitting the steering wheel angle request value to the filtering processing module.

The input signal of the filtering processing module is a steering wheel corner request value, the output signal is the final steering wheel corner, and the filtering processing module is used for carrying out low-pass filtering on the steering wheel corner request value so as to smoothly output the final steering wheel corner.

Fig. 5 is a block diagram showing a configuration of a vehicle trajectory tracking control device according to an exemplary embodiment of the present application. The device can be applied to the implementation environment shown in fig. 1 and is specifically configured in an intelligent terminal and a vehicle. The apparatus may also be applied to other exemplary implementation environments and specifically configured in other devices, and the embodiment does not limit the implementation environment to which the apparatus is applied.

As shown in fig. 5, the exemplary vehicle trajectory tracking control device 500 includes:

an obtaining module 501, configured to obtain target track parameters and state information of a vehicle, where the state information includes current speed information of the vehicle;

a preview module 502, configured to determine a preview distance according to the target track parameter and the vehicle speed information, and determine a preview deviation from a preview point to a target track according to the preview distance and the target track parameter;

a first control module 503, configured to construct a tracking control algorithm based on the understeer characteristic of the vehicle, input the preview distance and the preview deviation into the tracking control algorithm, and determine a first steering wheel angle value;

the second control module 504 is used for constructing an optimal control algorithm based on a self-adaptive weight control mode, inputting the preview distance and the preview deviation into the optimal control algorithm and determining a second steering wheel turning angle value;

a comprehensive control module 505, configured to perform weighted fusion on the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value;

and a track tracking module 506, configured to complete vehicle track tracking according to the control quantity corresponding to the comprehensive steering wheel angle value.

In the exemplary vehicle trajectory tracking control device, a tracking control algorithm is constructed based on understeer characteristics of the vehicle, the preview distance and the preview deviation are input into the tracking control algorithm, and a first steering wheel turning angle value is determined; constructing an optimal control algorithm based on an adaptive weight control mode, inputting the preview distance and the preview deviation into the optimal control algorithm, and determining a second steering wheel turning angle value; weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a comprehensive steering wheel angle value; on one hand, the trajectory tracking control algorithm based on the kinematics mechanism modeling is suitable for characteristic intervals with unobvious vehicle dynamics response, such as low speed, small curvature and the like; on the other hand, the optimal control algorithm based on the dynamics mechanism modeling is suitable for characteristic intervals with obvious vehicle dynamics response, such as high speed, large curvature and the like, fully considers the vehicle dynamics characteristics, and can be suitable for accurate tracking of the target track under all working conditions through dual-mode control comprehensive weighting processing.

It should be noted that the vehicle trajectory tracking control apparatus provided in the foregoing embodiment and the vehicle trajectory tracking control method provided in the foregoing embodiment belong to the same concept, and specific ways for the modules and units to perform operations have been described in detail in the method embodiments, and are not described herein again. In practical applications, the vehicle trajectory tracking control device provided in the above embodiment may distribute the functions to different functional modules according to needs, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above, which is not limited herein.

Referring to fig. 6, a schematic diagram of a vehicle trajectory tracking control device shown in the embodiment shown in fig. 5 is shown, and details are as follows:

the system comprises a track tracking controller module 4, an automatic driving main state machine information module 1, a track planning information module 2 and a CAN bus module 3 are input outside the track tracking controller module 4, the automatic driving main state machine information module 1 and the CAN bus module 3 are output, and the track tracking controller module 4 is composed of a state checking module 5, an information preprocessing module 6, a core algorithm module 12 and a post-processing module 11.

It should be noted that the core algorithm module 12 is composed of a first controller module 8, a second controller module 9 and a control integration module 10.

The input signals of the state check module 5 comprise the autopilot activation information ADSSts from the autopilot master state machine information module 1, the planning state information pcsts from the trajectory planning information module 2 and the steering machine state EPSSts from the CAN bus module 3. The state checking module 5 is configured to determine enabling information LATEnb of the core algorithm module 12 according to the autopilot activation information, the planning state and the steering engine state. When the automatic driving enable information ADSSts allows the trajectory tracking controller module 4 to be enabled, the plan state information pcsts is normal, and the steering state EPSSts is normal, the trajectory tracking controller module 4 is allowed to be activated.

The input signals of the information preprocessing module 6 include the target trajectory parameters LanePars from the trajectory planning information module 2, and the yaw rate information YawRate, the vehicle speed information VehSpd, the steering wheel angle position StrAng and the steering wheel handshake state epstrlsts from the CAN bus module 3. The information preprocessing module 6 is used for performing unit conversion on the input information, and performing outlier removal processing and first-order low-pass filtering processing by adopting a median filtering algorithm. The target track parameters LanePars comprise information of four aspects of lateral deviation, course angle, road curvature and road curvature change rate, the target track is in a cubic polynomial expression form, and the expression is as follows:

y＝A ₀ +A ₁ x+A ₂ x ² +A ₃ x ³ (1)

in the formula (1), y is a horizontal coordinate, x is a vertical coordinate, A ₀ ～A ₃ And coefficients of a cubic polynomial, namely, lateral deviation, course angle, 1/2 road curvature and road curvature change rate.

The input of the driver preview module 7 includes vehicle speed information VehSpd from the CAN bus module 3 and target trajectory parameters LanePars from the information preprocessing module 6. The driver preview module 7 is configured to calculate a preview distance prvdsi and a preview deviation PrvYErr from a preview point to a target trajectory according to the vehicle speed VehSpd, the preview time PrvT and the target trajectory parameter LanePars, where the expression is as follows:

in the formula (2), d _prv Is the pre-aiming distance PrvDis, v _x For vehicle speed VehSpd, t _prv For the preview time PrvT, y _prv Is the transverse deviation PrvYErr from the preview point to the target trajectory.

The input of the first controller module 8 is all input information from the core algorithm module 12 (i.e. enable information LATEnb, target trajectory parameters LanePars, yaw rate information yawrrate, vehicle speed information VehSpd) and the preview distance prvdsi, preview deviation PrvYErr information from the driver preview module 7. The first controller module 8 is based on kinematics mechanism modeling, calculates the expected steering wheel angle value ExpSW1 by adopting an improved pure tracking control algorithm, considers the understeer characteristic of the vehicle and corrects the understeer characteristic by a correction coefficient K _v To improve the trajectory tracking accuracy of the first controller module 8, the improved pure tracking control algorithm expression is as follows:

in the formula (3), δ _sw1 For the desired steering wheel angle value ExpSW1, L is the wheel base of the vehicle, i is the steering angle transmission ratio, v _xmin To set a minimum vehicle speed threshold, K _v Is the vehicle understeer characteristic coefficient, and pi is the circumferential ratio.

The inputs to the second controller module 9 include all input information from the core algorithm module 12 and the preview distance prvdsi, preview deviation prvdyerr information from the driver preview module 7. The second controller module 9 is modeled based on a dynamic mechanism, calculates an expected steering wheel angle value ExpSW2 by adopting an improved optimal control algorithm, and obtains an expected steering wheel angle control quantity by solving a linear stationary non-homogeneous equation in a self-adaptive weight control mode by adopting the improved optimal control algorithm. The self-adaptive weight control mode reduces the weight of the near-end preview point and improves the weight of the far-end preview point, so that the control frequency is reduced under the condition of ensuring the control precision as much as possible, the high-frequency jitter of control output is avoided, and the psychological trust of drivers and passengers is improved. The improved optimal control algorithm expression and solving process are as follows:

the expression of the state space equation:

wherein, in the formula (4), the state quantity x = [ y ψ v _y w _r ] ^T Representing lateral displacement, heading angle, lateral velocity and yaw rate, respectively, with y being the measured value. The state transition matrix A, the input matrix B and the output matrix C are respectively:

C＝[1 0 0 0] (7)

in the formulas (5), (6) and (7), M is the vehicle mass, k ₁ 、k ₂ Respectively front axle lateral deflection rigidity and rear axle lateral deflection rigidity, a and b respectively are the distances from the mass center of the automobile to the front axle and the rear axle, I _z The moment of inertia of the automobile around the z-axis at the centroid is shown.

Equation (4) is a linear stationary inhomogeneous equation set, assuming that the initial state is x at time t =0 ₀ Under the influence of input u, its time domain solution can be expressed as:

the measured values can be expressed as:

in the formula (9), F (t) and g (t) are intermediate variable functions.

According to the preview time set by the formula (2), the preview distance PrvDis is divided into n equal parts at equal intervals, which are equivalent to n preview points, and the weight of each equivalent preview point is as follows:

in the formula (10), w _j 、w _j1 Respectively, point j and point j-1.

Establishing an optimal control quadratic performance index function J through a preview deviation PrvYErr corresponding to the nth preview point and a measurement equation shown in an equation (9), wherein the expression is as follows:

the input u is subjected to partial derivation by equation (11), and the final optimal control input amount is obtained by setting dJ/du = 0:

the final optimal control input amount is obtained by discretizing the equations (10) and (9) and substituting the discretized equations into the equation (12):

in the formula, y _prvj Is the transverse deviation, delta, from the j-th pre-aiming point to the target track after the equivalence _sw2 The desired steering wheel angle value ExpSW2.

Inputs to the control integration module 10 include a desired steering wheel angle value ExpSW1 from the first controller module 8, a desired steering wheel angle value ExpSW2 from the second controller module 9, vehicle speed information VehSpd from the information preprocessing module 6, and a target trajectory parameter Lanepars. The control integration module 10 is used for adaptively adjusting the output weights of the first controller module 8 and the second controller module 9 according to the vehicle speed and the track curvature information, so as to calculate an integrated steering wheel angle expected value ExpSW, and the expression is as follows:

in the formula (14), from c ₁ 、c ₂ The output weights of the first controller module 8 and the second controller module 9, respectively, ρ is the curvature on the target trajectory corresponding to the current position of the vehicle, δ _sw To control the integrated steering wheel angle desired value ExpSW output by the integrated module 10,

p _ρ weighting coefficients with respect to the vehicle speed and the curvature, respectively, are expressed by the following equation:

the inputs of the post-processing module 11 include the integrated steering wheel angle desired value ExpSW from the control integration module 10, the vehicle speed information VehSpd from the information preprocessing module 6, the steering wheel angle position StrAng, and the steering gear handshake state epsctrlts. The post-processing module 11 is configured to switch the steering wheel angle request value according to the steering wheel handshake state, output a final steering wheel angle request value expeinalsw to the CAN bus module 3 through low-pass filtering and functional safety restriction, and output information latctrl sts indicating whether trajectory tracking control is valid to the automatic driving main state machine information module 1.

An embodiment of the present application further provides an electronic device, including: one or more processors; a storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic device to implement the vehicle trajectory tracking control method provided in the above-described embodiments.

FIG. 7 illustrates a schematic structural diagram of a computer system suitable for use to implement the electronic device of the embodiments of the subject application. It should be noted that the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.

As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for system operation are also stored. The CPU701, the ROM702, and the RAM703 are connected to each other via a bus 704. An Input/Output (I/O) interface 705 is also connected to the bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 708 including a hard disk and the like; and a communication section 709 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that the computer program read out therefrom is mounted into the storage section 708 as necessary.

In particular, according to embodiments of the present application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. When the computer program is executed by a Central Processing Unit (CPU) 701, various functions defined in the system of the present application are executed.

It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.

Yet another aspect of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a vehicle trajectory tracking control method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist separately without being incorporated in the electronic device.

The above-described embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and technical spirit of the present disclosure be covered by the claims of the present application.

Claims

1. A vehicle trajectory tracking control method, characterized by comprising:

2. The vehicle trajectory tracking control method according to claim 1, wherein the constructing a tracking control algorithm based on the understeer characteristics of the vehicle further comprises:

3. The vehicle trajectory tracking control method according to claim 1, wherein the step of constructing an optimal control algorithm based on an adaptive weight control method, inputting the preview distance and the preview deviation into the optimal control algorithm, and determining a second steering wheel turning angle value further comprises the steps of:

4. The vehicle track following control method according to claim 1 or 2, characterized in that the expression of the first steering wheel angle value is determined as:

5. The vehicle trajectory tracking control method according to claim 1, wherein the step of determining a preview distance according to the target trajectory parameter and the vehicle speed information, and determining a preview deviation from a preview point to a target trajectory according to the preview distance and the target trajectory parameter further comprises the steps of:

6. The vehicle trajectory tracking control method according to claim 3, characterized in that the expression of the performance index function is determined as:

7. The vehicle track following control method according to claim 1 or 3, characterized in that the expression of the second steering wheel angle value is determined as:

8. The vehicle trajectory tracking control method according to any one of claims 1 to 3, wherein the weighting and fusing the first steering wheel angle value and the second steering wheel angle value to obtain a composite steering wheel angle value includes:

according to the current speed information of the vehicle and the curvature of a target track corresponding to the current position of the vehicle, respectively determining a first weight coefficient corresponding to the tracking control algorithm and a second weight coefficient corresponding to the optimal control algorithm;

performing weighted calculation on the first steering wheel angle value and a first weight coefficient, and determining a weighted first steering wheel angle value;

performing weighted calculation on the second steering wheel angle value and a second weight coefficient to determine a weighted second steering wheel angle value;

9. The vehicle track following control method according to claim 8, wherein the method further includes at least one of the following steps according to the current vehicle speed information of the vehicle and the curvature of the target track corresponding to the current position of the vehicle:

wherein v is _x As the current vehicle speed information of the vehicle,

p _ρ determines the magnitude of the second weight coefficient.

10. The vehicle trajectory tracking control method according to any one of claims 1 to 3, wherein an expression corresponding to the target trajectory in the target trajectory parameters is determined as:

y＝A ₀ +A ₁ x+A ₂ x+A ₃ x ³

11. The vehicle trajectory tracking control method according to any one of claims 1 to 3, wherein the acquiring target trajectory parameters and state information of the vehicle further includes:

12. The vehicle trajectory tracking control method according to any one of claims 1 to 3, before the obtaining of the target trajectory parameter and the state information of the vehicle, further comprising:

13. The vehicle trajectory tracking control method according to claim 11, further comprising, after completing vehicle trajectory tracking based on the control amount corresponding to the integrated steering wheel angle value:

14. A vehicle trajectory tracking control device characterized by comprising:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring target track parameters and state information of a vehicle, and the state information comprises current speed information of the vehicle;

and the track tracking module is used for finishing vehicle track tracking according to the control quantity corresponding to the comprehensive steering wheel rotating angle value.

15. An electronic device, comprising:

one or more processors;

a storage device to store one or more programs that, when executed by the one or more processors, cause the electronic equipment to implement the vehicle trajectory tracking control method of any one of claims 1 to 13.

16. A vehicular apparatus characterized by comprising the electronic apparatus of claim 15.

17. A computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to execute the vehicle trajectory tracking control method of any one of claims 1 to 13.